Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3661
Missing cells6626
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory632.2 B

Variable types

Categorical10
Text3
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (55.9%) Imbalance
others is highly imbalanced (50.1%) Imbalance
facing has 1039 (28.4%) missing values Missing
super_built_up_area has 1786 (48.8%) missing values Missing
built_up_area has 1987 (54.3%) missing values Missing
carpet_area has 1791 (48.9%) missing values Missing
area is highly skewed (γ1 = 29.73095613) Skewed
built_up_area is highly skewed (γ1 = 40.52309524) Skewed
carpet_area is highly skewed (γ1 = 24.32031436) Skewed
floorNum has 129 (3.5%) zeros Zeros
luxury_score has 459 (12.5%) zeros Zeros

Reproduction

Analysis started2025-04-10 13:25:26.870138
Analysis finished2025-04-10 13:25:43.795221
Duration16.93 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size247.5 KiB
flat
2818 
house
843 

Length

Max length5
Median length4
Mean length4.230265
Min length4

Characters and Unicode

Total characters15487
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2818
77.0%
house 843
 
23.0%

Length

2025-04-10T13:25:43.897204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T13:25:43.976282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
flat 2818
77.0%
house 843
 
23.0%

Most occurring characters

ValueCountFrequency (%)
f 2818
18.2%
l 2818
18.2%
a 2818
18.2%
t 2818
18.2%
h 843
 
5.4%
o 843
 
5.4%
u 843
 
5.4%
s 843
 
5.4%
e 843
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15487
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 2818
18.2%
l 2818
18.2%
a 2818
18.2%
t 2818
18.2%
h 843
 
5.4%
o 843
 
5.4%
u 843
 
5.4%
s 843
 
5.4%
e 843
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15487
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 2818
18.2%
l 2818
18.2%
a 2818
18.2%
t 2818
18.2%
h 843
 
5.4%
o 843
 
5.4%
u 843
 
5.4%
s 843
 
5.4%
e 843
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15487
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 2818
18.2%
l 2818
18.2%
a 2818
18.2%
t 2818
18.2%
h 843
 
5.4%
o 843
 
5.4%
u 843
 
5.4%
s 843
 
5.4%
e 843
 
5.4%
Distinct675
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size292.7 KiB
2025-04-10T13:25:44.285405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.873497
Min length1

Characters and Unicode

Total characters61757
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowla vida by tata housing
2nd rowumang monsoon breeze
3rd rowireo the grand arch
4th rowm3m woodshire
5th rowambience creacions
ValueCountFrequency (%)
independent 486
 
5.0%
the 350
 
3.6%
dlf 219
 
2.3%
park 209
 
2.2%
city 163
 
1.7%
global 153
 
1.6%
m3m 152
 
1.6%
emaar 151
 
1.6%
signature 150
 
1.6%
heights 134
 
1.4%
Other values (783) 7473
77.5%
2025-04-10T13:25:44.780838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6675
 
10.8%
5982
 
9.7%
a 5840
 
9.5%
r 4156
 
6.7%
n 4140
 
6.7%
t 3703
 
6.0%
s 3465
 
5.6%
i 3331
 
5.4%
l 2929
 
4.7%
o 2746
 
4.4%
Other values (32) 18790
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6675
 
10.8%
5982
 
9.7%
a 5840
 
9.5%
r 4156
 
6.7%
n 4140
 
6.7%
t 3703
 
6.0%
s 3465
 
5.6%
i 3331
 
5.4%
l 2929
 
4.7%
o 2746
 
4.4%
Other values (32) 18790
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6675
 
10.8%
5982
 
9.7%
a 5840
 
9.5%
r 4156
 
6.7%
n 4140
 
6.7%
t 3703
 
6.0%
s 3465
 
5.6%
i 3331
 
5.4%
l 2929
 
4.7%
o 2746
 
4.4%
Other values (32) 18790
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6675
 
10.8%
5982
 
9.7%
a 5840
 
9.5%
r 4156
 
6.7%
n 4140
 
6.7%
t 3703
 
6.0%
s 3465
 
5.6%
i 3331
 
5.4%
l 2929
 
4.7%
o 2746
 
4.4%
Other values (32) 18790
30.4%

sector
Text

Distinct113
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size265.7 KiB
2025-04-10T13:25:45.059892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3223163
Min length7

Characters and Unicode

Total characters34129
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 113
2nd rowsector 78
3rd rowsector 58
4th rowsector 107
5th rowsector 22
ValueCountFrequency (%)
sector 3436
46.7%
road 178
 
2.4%
sohna 166
 
2.3%
85 108
 
1.5%
102 107
 
1.5%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
65 87
 
1.2%
81 87
 
1.2%
Other values (106) 2899
39.4%
2025-04-10T13:25:45.459123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3791
11.1%
3689
10.8%
s 3681
10.8%
r 3681
10.8%
e 3526
10.3%
c 3487
10.2%
t 3447
10.1%
1 1071
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6176
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3791
11.1%
3689
10.8%
s 3681
10.8%
r 3681
10.8%
e 3526
10.3%
c 3487
10.2%
t 3447
10.1%
1 1071
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6176
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3791
11.1%
3689
10.8%
s 3681
10.8%
r 3681
10.8%
e 3526
10.3%
c 3487
10.2%
t 3447
10.1%
1 1071
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6176
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3791
11.1%
3689
10.8%
s 3681
10.8%
r 3681
10.8%
e 3526
10.3%
c 3487
10.2%
t 3447
10.1%
1 1071
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6176
18.1%

price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)12.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.5336639
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2025-04-10T13:25:45.587623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9806235
Coefficient of variation (CV)1.1764084
Kurtosis14.933373
Mean2.5336639
Median Absolute Deviation (MAD)0.72
Skewness3.2791705
Sum9273.21
Variance8.8841164
MonotonicityNot monotonic
2025-04-10T13:25:45.723713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.6%
0.95 52
 
1.4%
2 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.5%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2651
Distinct (%)72.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13892.668
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2025-04-10T13:25:45.858109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715.95
Q16817.25
median9020
Q313880.5
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063.25

Descriptive statistics

Standard deviation23210.067
Coefficient of variation (CV)1.6706702
Kurtosis186.92801
Mean13892.668
Median Absolute Deviation (MAD)2794
Skewness11.43719
Sum50847166
Variance5.3870722 × 108
MonotonicityNot monotonic
2025-04-10T13:25:46.009956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
22222 13
 
0.4%
6666 13
 
0.4%
11111 13
 
0.4%
8333 12
 
0.3%
7500 12
 
0.3%
33333 11
 
0.3%
Other values (2641) 3509
95.8%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1312
Distinct (%)35.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2888.3311
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2025-04-10T13:25:46.488371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile518.85
Q11232.25
median1733
Q32300
95-th percentile4246.2
Maximum875000
Range874950
Interquartile range (IQR)1067.75

Descriptive statistics

Standard deviation23167.506
Coefficient of variation (CV)8.0210699
Kurtosis942.02903
Mean2888.3311
Median Absolute Deviation (MAD)533
Skewness29.730956
Sum10571292
Variance5.3673333 × 108
MonotonicityNot monotonic
2025-04-10T13:25:46.641276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
3240 43
 
1.2%
1950 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3267
89.2%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%
Distinct2350
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Memory size426.5 KiB
2025-04-10T13:25:47.073668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.294455
Min length12

Characters and Unicode

Total characters198772
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1847 ?
Unique (%)50.5%

Sample

1st rowSuper Built up area 2691(250 sq.m.)Built Up area: 2460 sq.ft. (228.54 sq.m.)Carpet area: 2100 sq.ft. (195.1 sq.m.)
2nd rowSuper Built up area 1854(172.24 sq.m.)Built Up area: 1668 sq.ft. (154.96 sq.m.)Carpet area: 1501 sq.ft. (139.45 sq.m.)
3rd rowCarpet area: 2864 (266.07 sq.m.)
4th rowSuper Built up area 2361(219.34 sq.m.)
5th rowCarpet area: 3000 (278.71 sq.m.)
ValueCountFrequency (%)
area 5552
18.5%
sq.m 3639
12.1%
up 3015
 
10.0%
built 2314
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1183
 
3.9%
sq.m.)built 699
 
2.3%
carpet 683
 
2.3%
plot 667
 
2.2%
Other values (2842) 8667
28.8%
2025-04-10T13:25:47.687365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26384
 
13.3%
. 20321
 
10.2%
a 13105
 
6.6%
r 9428
 
4.7%
e 9297
 
4.7%
1 9195
 
4.6%
s 7536
 
3.8%
q 7405
 
3.7%
t 7303
 
3.7%
u 6765
 
3.4%
Other values (25) 82033
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 198772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26384
 
13.3%
. 20321
 
10.2%
a 13105
 
6.6%
r 9428
 
4.7%
e 9297
 
4.7%
1 9195
 
4.6%
s 7536
 
3.8%
q 7405
 
3.7%
t 7303
 
3.7%
u 6765
 
3.4%
Other values (25) 82033
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 198772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26384
 
13.3%
. 20321
 
10.2%
a 13105
 
6.6%
r 9428
 
4.7%
e 9297
 
4.7%
1 9195
 
4.6%
s 7536
 
3.8%
q 7405
 
3.7%
t 7303
 
3.7%
u 6765
 
3.4%
Other values (25) 82033
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 198772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26384
 
13.3%
. 20321
 
10.2%
a 13105
 
6.6%
r 9428
 
4.7%
e 9297
 
4.7%
1 9195
 
4.6%
s 7536
 
3.8%
q 7405
 
3.7%
t 7303
 
3.7%
u 6765
 
3.4%
Other values (25) 82033
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3477192
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2025-04-10T13:25:47.796968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8798873
Coefficient of variation (CV)0.56154271
Kurtosis18.507139
Mean3.3477192
Median Absolute Deviation (MAD)1
Skewness3.5006609
Sum12256
Variance3.5339764
MonotonicityNot monotonic
2025-04-10T13:25:47.918155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.9%
2 941
25.7%
4 659
18.0%
5 200
 
5.5%
1 124
 
3.4%
6 73
 
2.0%
9 40
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 27
 
0.7%
Other values (9) 43
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 941
25.7%
3 1496
40.9%
4 659
18.0%
5 200
 
5.5%
6 73
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 40
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 11
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 27
0.7%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4127288
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2025-04-10T13:25:48.023828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9290253
Coefficient of variation (CV)0.56524424
Kurtosis17.902203
Mean3.4127288
Median Absolute Deviation (MAD)1
Skewness3.265409
Sum12494
Variance3.7211385
MonotonicityNot monotonic
2025-04-10T13:25:48.135916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1076
29.4%
2 1046
28.6%
4 818
22.3%
5 289
 
7.9%
1 155
 
4.2%
6 117
 
3.2%
9 40
 
1.1%
7 38
 
1.0%
8 24
 
0.7%
12 21
 
0.6%
Other values (9) 37
 
1.0%
ValueCountFrequency (%)
1 155
 
4.2%
2 1046
28.6%
3 1076
29.4%
4 818
22.3%
5 289
 
7.9%
6 117
 
3.2%
7 38
 
1.0%
8 24
 
0.7%
9 40
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 7
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 21
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
3+
1161 
3
1073 
2
882 
1
364 
0
181 

Length

Max length2
Median length1
Mean length1.3171265
Min length1

Characters and Unicode

Total characters4822
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3+
2nd row3
3rd row3
4th row3+
5th row3+

Common Values

ValueCountFrequency (%)
3+ 1161
31.7%
3 1073
29.3%
2 882
24.1%
1 364
 
9.9%
0 181
 
4.9%

Length

2025-04-10T13:25:48.263023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T13:25:48.342922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 2234
61.0%
2 882
 
24.1%
1 364
 
9.9%
0 181
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2234
46.3%
+ 1161
24.1%
2 882
 
18.3%
1 364
 
7.5%
0 181
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2234
46.3%
+ 1161
24.1%
2 882
 
18.3%
1 364
 
7.5%
0 181
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2234
46.3%
+ 1161
24.1%
2 882
 
18.3%
1 364
 
7.5%
0 181
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2234
46.3%
+ 1161
24.1%
2 882
 
18.3%
1 364
 
7.5%
0 181
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.8163097
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2025-04-10T13:25:48.460267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0191061
Coefficient of variation (CV)0.88304468
Kurtosis4.4935142
Mean6.8163097
Median Absolute Deviation (MAD)3
Skewness1.6881491
Sum24825
Variance36.229638
MonotonicityNot monotonic
2025-04-10T13:25:48.634395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 493
13.5%
2 488
13.3%
1 349
 
9.5%
4 312
 
8.5%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 937
25.6%
ValueCountFrequency (%)
0 129
 
3.5%
1 349
9.5%
2 488
13.3%
3 493
13.5%
4 312
8.5%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.3%
Missing1039
Missing (%)28.4%
Memory size257.0 KiB
East
621 
North-East
621 
North
386 
West
247 
South
231 
Other values (3)
516 

Length

Max length10
Median length5
Mean length6.8371472
Min length4

Characters and Unicode

Total characters17927
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast
2nd rowNorth-West
3rd rowNorth-West
4th rowEast
5th rowSouth-East

Common Values

ValueCountFrequency (%)
East 621
17.0%
North-East 621
17.0%
North 386
 
10.5%
West 247
 
6.7%
South 231
 
6.3%
North-West 192
 
5.2%
South-East 171
 
4.7%
South-West 153
 
4.2%
(Missing) 1039
28.4%

Length

2025-04-10T13:25:48.767634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T13:25:48.883728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
east 621
23.7%
north-east 621
23.7%
north 386
14.7%
west 247
 
9.4%
south 231
 
8.8%
north-west 192
 
7.3%
south-east 171
 
6.5%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3759
21.0%
s 2005
11.2%
o 1754
9.8%
h 1754
9.8%
E 1413
 
7.9%
a 1413
 
7.9%
N 1199
 
6.7%
r 1199
 
6.7%
- 1137
 
6.3%
W 592
 
3.3%
Other values (3) 1702
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3759
21.0%
s 2005
11.2%
o 1754
9.8%
h 1754
9.8%
E 1413
 
7.9%
a 1413
 
7.9%
N 1199
 
6.7%
r 1199
 
6.7%
- 1137
 
6.3%
W 592
 
3.3%
Other values (3) 1702
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3759
21.0%
s 2005
11.2%
o 1754
9.8%
h 1754
9.8%
E 1413
 
7.9%
a 1413
 
7.9%
N 1199
 
6.7%
r 1199
 
6.7%
- 1137
 
6.3%
W 592
 
3.3%
Other values (3) 1702
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3759
21.0%
s 2005
11.2%
o 1754
9.8%
h 1754
9.8%
E 1413
 
7.9%
a 1413
 
7.9%
N 1199
 
6.7%
r 1199
 
6.7%
- 1137
 
6.3%
W 592
 
3.3%
Other values (3) 1702
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size280.2 KiB
Relatively New
1640 
New Property
590 
Moderately Old
558 
Undefined
313 
Old Property
302 

Length

Max length18
Median length14
Mean length13.367113
Min length9

Characters and Unicode

Total characters48937
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowModerately Old
3rd rowRelatively New
4th rowRelatively New
5th rowNew Property

Common Values

ValueCountFrequency (%)
Relatively New 1640
44.8%
New Property 590
 
16.1%
Moderately Old 558
 
15.2%
Undefined 313
 
8.5%
Old Property 302
 
8.2%
Under Construction 258
 
7.0%

Length

2025-04-10T13:25:49.015395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T13:25:49.122627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
new 2230
31.8%
relatively 1640
23.4%
property 892
 
12.7%
old 860
 
12.3%
moderately 558
 
8.0%
undefined 313
 
4.5%
under 258
 
3.7%
construction 258
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e 8402
17.2%
l 4698
 
9.6%
t 3606
 
7.4%
3348
 
6.8%
y 3090
 
6.3%
r 2858
 
5.8%
d 2302
 
4.7%
N 2230
 
4.6%
w 2230
 
4.6%
i 2211
 
4.5%
Other values (15) 13962
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48937
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8402
17.2%
l 4698
 
9.6%
t 3606
 
7.4%
3348
 
6.8%
y 3090
 
6.3%
r 2858
 
5.8%
d 2302
 
4.7%
N 2230
 
4.6%
w 2230
 
4.6%
i 2211
 
4.5%
Other values (15) 13962
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48937
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8402
17.2%
l 4698
 
9.6%
t 3606
 
7.4%
3348
 
6.8%
y 3090
 
6.3%
r 2858
 
5.8%
d 2302
 
4.7%
N 2230
 
4.6%
w 2230
 
4.6%
i 2211
 
4.5%
Other values (15) 13962
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48937
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8402
17.2%
l 4698
 
9.6%
t 3606
 
7.4%
3348
 
6.8%
y 3090
 
6.3%
r 2858
 
5.8%
d 2302
 
4.7%
N 2230
 
4.6%
w 2230
 
4.6%
i 2211
 
4.5%
Other values (15) 13962
28.5%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct593
Distinct (%)31.6%
Missing1786
Missing (%)48.8%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2025-04-10T13:25:49.275092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.5
Variance583959.12
MonotonicityNot monotonic
2025-04-10T13:25:49.425967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 37
 
1.0%
1650 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
2408 19
 
0.5%
1900 19
 
0.5%
1930 18
 
0.5%
1350 17
 
0.5%
Other values (583) 1634
44.6%
(Missing) 1786
48.8%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct641
Distinct (%)38.3%
Missing1987
Missing (%)54.3%
Infinite0
Infinite (%)0.0%
Mean2386.1989
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2025-04-10T13:25:49.578042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.65
Q11110.5
median1650
Q32400
95-th percentile4660.5
Maximum737147
Range737145
Interquartile range (IQR)1289.5

Descriptive statistics

Standard deviation18026.927
Coefficient of variation (CV)7.5546621
Kurtosis1652.6076
Mean2386.1989
Median Absolute Deviation (MAD)619.5
Skewness40.523095
Sum3994497
Variance3.2497009 × 108
MonotonicityNot monotonic
2025-04-10T13:25:49.719254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 32
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
2000 24
 
0.7%
1300 24
 
0.7%
1700 23
 
0.6%
Other values (631) 1372
37.5%
(Missing) 1987
54.3%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct732
Distinct (%)39.1%
Missing1791
Missing (%)48.9%
Infinite0
Infinite (%)0.0%
Mean2531.5396
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2025-04-10T13:25:49.868180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1845
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)945

Descriptive statistics

Standard deviation22811.918
Coefficient of variation (CV)9.0110847
Kurtosis603.89238
Mean2531.5396
Median Absolute Deviation (MAD)470
Skewness24.320314
Sum4733979
Variance5.2038359 × 108
MonotonicityNot monotonic
2025-04-10T13:25:50.008663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1600 35
 
1.0%
1800 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1000 22
 
0.6%
1450 22
 
0.6%
Other values (722) 1576
43.0%
(Missing) 1791
48.9%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.0 KiB
0
2964 
1
697 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3661
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2964
81.0%
1 697
 
19.0%

Length

2025-04-10T13:25:50.137537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T13:25:50.211013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2964
81.0%
1 697
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 2964
81.0%
1 697
 
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2964
81.0%
1 697
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2964
81.0%
1 697
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2964
81.0%
1 697
 
19.0%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.0 KiB
0
2342 
1
1319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3661
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2342
64.0%
1 1319
36.0%

Length

2025-04-10T13:25:50.301016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T13:25:50.376346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2342
64.0%
1 1319
36.0%

Most occurring characters

ValueCountFrequency (%)
0 2342
64.0%
1 1319
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2342
64.0%
1 1319
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2342
64.0%
1 1319
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2342
64.0%
1 1319
36.0%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.0 KiB
0
3326 
1
335 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3661
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3326
90.8%
1 335
 
9.2%

Length

2025-04-10T13:25:50.472380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T13:25:50.541621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3326
90.8%
1 335
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3326
90.8%
1 335
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3326
90.8%
1 335
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3326
90.8%
1 335
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3326
90.8%
1 335
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.0 KiB
0
3011 
1
650 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3661
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3011
82.2%
1 650
 
17.8%

Length

2025-04-10T13:25:50.643878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T13:25:50.713188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3011
82.2%
1 650
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3011
82.2%
1 650
 
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3011
82.2%
1 650
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3011
82.2%
1 650
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3011
82.2%
1 650
 
17.8%

others
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.0 KiB
0
3259 
1
402 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3661
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3259
89.0%
1 402
 
11.0%

Length

2025-04-10T13:25:50.798864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T13:25:50.870455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3259
89.0%
1 402
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3259
89.0%
1 402
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3259
89.0%
1 402
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3259
89.0%
1 402
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3259
89.0%
1 402
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.0 KiB
0
2403 
2
1055 
1
 
203

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3661
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 2403
65.6%
2 1055
28.8%
1 203
 
5.5%

Length

2025-04-10T13:25:50.959922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T13:25:51.034375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2403
65.6%
2 1055
28.8%
1 203
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 2403
65.6%
2 1055
28.8%
1 203
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2403
65.6%
2 1055
28.8%
1 203
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2403
65.6%
2 1055
28.8%
1 203
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2403
65.6%
2 1055
28.8%
1 203
 
5.5%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.544387
Minimum0
Maximum174
Zeros459
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2025-04-10T13:25:51.142982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.062737
Coefficient of variation (CV)0.74167576
Kurtosis-0.87973169
Mean71.544387
Median Absolute Deviation (MAD)38
Skewness0.45928832
Sum261924
Variance2815.6541
MonotonicityNot monotonic
2025-04-10T13:25:51.294315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 459
 
12.5%
49 347
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
165 55
 
1.5%
38 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
42 45
 
1.2%
37 45
 
1.2%
Other values (151) 2301
62.9%
ValueCountFrequency (%)
0 459
12.5%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 42
 
1.1%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 27
 
0.7%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-04-10T13:25:41.009334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:29.040362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:30.752953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:32.255105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:33.420539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:34.660743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:36.098390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:37.231115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:38.416346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:39.861013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:41.148459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:29.221688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:30.938030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:32.362964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:33.542066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:34.785932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:36.205106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:37.380337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:38.536118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:39.976773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:41.347642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:29.383934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:31.110493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:32.470810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:33.666148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:34.905644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:36.313761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:37.496549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:38.651029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:40.095154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:41.506087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:29.535177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:31.224887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:32.567325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:33.784540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:35.017451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:36.415596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:37.605520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:38.772417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:40.225477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:41.677168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:29.709533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:31.345080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:32.675097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:33.913433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:35.369932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:36.528098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:37.722011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:38.898637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:40.344634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:41.852916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:29.909412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:31.465106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:32.797523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:34.051928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:35.486915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:36.642353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:37.847972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:39.023954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:40.460578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:42.002124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:30.052347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:31.571449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:32.906780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:34.176721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:35.600591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:36.744909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:37.950914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:39.136735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:40.571447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:42.158372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:30.212508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:31.869165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:33.049848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:34.298373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:35.714970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:36.853641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:38.057778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:39.509727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:40.674031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:42.340417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:30.398921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:32.008552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:33.169574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:34.423045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:35.838347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:36.976026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:38.182461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:39.628858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:40.778412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:42.509762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:30.568312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:32.132139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:33.296924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:34.536603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:35.957483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:37.101360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:38.300698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:39.728155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-10T13:25:40.891057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-10T13:25:51.422501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2710.1100.1290.0000.0000.0920.1270.2140.2540.1060.1870.1020.0550.3910.2860.1420.1340.086
area0.0001.0000.0110.6870.6240.8350.8010.0220.1160.0430.2590.0420.0370.7440.2070.0280.0150.0390.0180.948
balcony0.2710.0111.0000.2240.1740.0000.0260.0160.0790.1780.2240.0800.1940.1360.0330.2120.4390.1440.1800.306
bathroom0.1100.6870.2241.0000.8620.4860.6040.039-0.0030.1960.1800.0640.2840.7200.4110.4690.5190.2440.1700.819
bedRoom0.1290.6240.1740.8621.0000.3970.5740.029-0.1000.1670.0580.0730.2880.6810.4170.5920.3160.2240.1480.800
built_up_area0.0000.8350.0000.4860.3971.0000.9691.0000.0900.0890.2940.0000.0000.6050.1320.0000.0000.0000.0000.926
carpet_area0.0000.8010.0260.6040.5740.9691.0000.0000.1570.0000.2390.0170.0000.6130.1360.0000.0000.0000.0040.894
facing0.0920.0220.0160.0390.0291.0000.0001.0000.0000.0490.0650.0000.0320.0210.0000.0930.0350.0370.0000.000
floorNum0.1270.1160.079-0.003-0.1000.0900.1570.0001.0000.0190.2320.0330.1010.001-0.1260.4820.0860.1110.0760.152
furnishing_type0.2140.0430.1780.1960.1670.0890.0000.0490.0191.0000.2440.0570.2160.1760.0230.0790.2710.1550.1400.133
luxury_score0.2540.2590.2240.1800.0580.2940.2390.0650.2320.2441.0000.1750.1870.2150.0540.3300.3460.2290.1800.222
others0.1060.0420.0800.0640.0730.0000.0170.0000.0330.0570.1751.0000.0280.0340.0360.0240.0000.1060.0260.084
pooja room0.1870.0370.1940.2840.2880.0000.0000.0320.1010.2160.1870.0281.0000.3340.0430.2500.2490.3050.3090.157
price0.1020.7440.1360.7200.6810.6050.6130.0210.0010.1760.2150.0340.3341.0000.7440.5430.3690.3030.2440.772
price_per_sqft0.0550.2070.0330.4110.4170.1320.1360.000-0.1260.0230.0540.0360.0430.7441.0000.2010.0440.0000.0300.287
property_type0.3910.0280.2120.4690.5920.0000.0000.0930.4820.0790.3300.0240.2500.5430.2011.0000.0620.2410.1231.000
servant room0.2860.0150.4390.5190.3160.0000.0000.0350.0860.2710.3460.0000.2490.3690.0440.0621.0000.1590.1810.584
store room0.1420.0390.1440.2440.2240.0000.0000.0370.1110.1550.2290.1060.3050.3030.0000.2410.1591.0000.2260.046
study room0.1340.0180.1800.1700.1480.0000.0040.0000.0760.1400.1800.0260.3090.2440.0300.1230.1810.2261.0000.121
super_built_up_area0.0860.9480.3060.8190.8000.9260.8940.0000.1520.1330.2220.0840.1570.7720.2871.0000.5840.0460.1211.000

Missing values

2025-04-10T13:25:42.805439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-10T13:25:43.155751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-10T13:25:43.599023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatla vida by tata housingsector 1133.0014285.02100.0Super Built up area 2691(250 sq.m.)Built Up area: 2460 sq.ft. (228.54 sq.m.)Carpet area: 2100 sq.ft. (195.1 sq.m.)333+3.0EastRelatively New2691.02460.02100.0010000167
1flatumang monsoon breezesector 780.884746.01854.0Super Built up area 1854(172.24 sq.m.)Built Up area: 1668 sq.ft. (154.96 sq.m.)Carpet area: 1501 sq.ft. (139.45 sq.m.)3438.0North-WestModerately Old1854.01668.01501.0010100111
2flatireo the grand archsector 584.8516934.02864.0Carpet area: 2864 (266.07 sq.m.)45323.0North-WestRelatively NewNaNNaN2864.011011249
3flatm3m woodshiresector 1071.496310.02361.0Super Built up area 2361(219.34 sq.m.)343+5.0EastRelatively New2361.0NaNNaN011002174
4flatambience creacionssector 226.1620533.03000.0Carpet area: 3000 (278.71 sq.m.)453+10.0South-EastNew PropertyNaNNaN3000.001010249
5flatgodrej ariasector 791.459062.01600.0Super Built up area 1495(138.89 sq.m.)Built Up area: 1494 sq.ft. (138.8 sq.m.)223+9.0EastRelatively New1495.01494.0NaN100000113
6flattulip violetsector 691.559822.01578.0Super Built up area 1578(146.6 sq.m.)3329.0North-EastRelatively New1578.0NaNNaN000102165
7flatsmart world gemssector 891.208432.01423.0Carpet area: 1423 (132.2 sq.m.)3322.0North-EastNew PropertyNaNNaN1423.000000044
8flatkiran residencysector 561.557750.02000.0Super Built up area 2000(185.81 sq.m.)3335.0NorthOld Property2000.0NaNNaN01000252
9houseemaar mgf marbellasector 6612.0038095.03150.0Plot area 350(292.64 sq.m.)563+3.0North-EastRelatively NewNaN3150.0NaN011002153
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3788houseIndependentsector 2513.5037313.03618.0Plot area 402(336.12 sq.m.)563+4.0North-EastOld PropertyNaN3618.0NaN00001279
3789housegreenopolissector 890.705397.01297.0Built Up area: 1297 (120.5 sq.m.)22214.0North-EastUndefinedNaN1297.0NaN0000000
3791flatemaar digihomessector 622.1014254.01473.0Super Built up area 1508.26(140.12 sq.m.)22230.0NorthNew Property1508.26NaNNaN01000052
3792flatdlf new town heightssector 901.806600.02727.0Super Built up area 2727(253.35 sq.m.)443+23.0NaNModerately Old2727.00NaNNaN00000067
3793houseinternational city by sobha phase 2sector 1098.4823556.03600.0Plot area 400(334.45 sq.m.)463+3.0North-EastNew PropertyNaN3600.0NaN111100153
3794flatsignature global park 4sector 361.009900.01010.0Carpet area: 1010 (93.83 sq.m.)3232.0NaNNew PropertyNaNNaN1010.0000000128
3795housesuncity essel towerssector 286.0022222.02700.0Plot area 300(250.84 sq.m.)443+3.0EastOld PropertyNaN2700.0NaN10000289
3796flatsobha citysector 1084.1023401.01752.0Super Built up area 2344(217.76 sq.m.)Built Up area: 2343 sq.ft. (217.67 sq.m.)Carpet area: 1752 sq.ft. (162.77 sq.m.)45325.0North-EastNew Property2344.002343.01752.011000197
3797flatvatika emilia floorssector 830.605714.01050.0Super Built up area 1050(97.55 sq.m.)Built Up area: 990 sq.ft. (91.97 sq.m.)Carpet area: 950 sq.ft. (88.26 sq.m.)2221.0SouthRelatively New1050.00990.0950.0000002167
3798flatdlf new town heightssector 901.556556.02364.0Super Built up area 2364(219.62 sq.m.)443+1.0South-EastRelatively New2364.00NaNNaN01010081